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L05_In-class_Examples-1
Course: ES 140, Spring 2009School: Vanderbilt
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Word Count: 126
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(nm) Thickness Time (h) (1-exp(-0.5*Time)) 0 0 0 450 1 0.39 700 2 0.63 1200 4 0.86 1550 6 0.95 1890 8 0.98 2200 10 0.99 2500 12 1 2780 14 1 3060 16 1 SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 0.87 Standard Error 20.79 Observations 10 ANOVA df Regression Residual Total 2 8 10 SS 36285642.58 3457.42 36289100 MS ### 432.18 F Significance F ### 0 Intercept X Variable 1 X Variable...
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(nm) Thickness Time (h) (1-exp(-0.5*Time)) 0 0 0 450 1 0.39 700 2 0.63 1200 4 0.86 1550 6 0.95 1890 8 0.98 2200 10 0.99 2500 12 1 2780 14 1 3060 16 1
SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 0.87 Standard Error 20.79 Observations 10 ANOVA df Regression Residual Total 2 8 10 SS 36285642.58 3457.42 36289100 MS ### 432.18 F Significance F ### 0
Intercept X Variable 1 X Variable 2
Coefficients Standard Error t Stat P-valueLower 95%pper 95% U Lower 95.0% 0 #N/A #N/A #N/A #N/A #N/A #N/A 148.84 1.89 78.61 0 144.47 153.21 144.47 699.41 20.24 34.55 0 652.72 746.09 652.72
RESIDUAL OUTPUT Observation Predicted Y 1 0 2 424.04 3 739.79 4 1200.11 5 1557.63 6 1877.32 7 2183.1 Residuals 0 25.96 -39.79 -0.11 -7.63 16.9
8 12.68 9 10
2483.76 2782.54 3080.62
16.24 -2.54 -20.62
X Variable 1 Residual Plot
30 25 20 15
X Variable 2 Residual Plot
30 25 20 15
Residuals
10 5 0 -5 -10 -15 -20 -25 -30 -35 -40 0 2
Residuals
10 5 0 -5 -10 -15 -20 -25 -30 -35 -40 0 3250 3000 2750 2500 2250 2000 1 4 6 8 10 12 14 16
X Variable 1 Line Fit Plot
Y Predicted Y
Upper 95.0% #N/A 153.21 746.09
Y
1750 1500 1250 1000 750 500 250 0 0 1 2 4 6 8 10 12 14 16
X Variable 1
X Variable 2 Line Fit Plot
3250 3000 2750 2500 2250 2000 1750 1500 1250 1000 750 500 250 0 0 0.393 46934 02873 0.632 12055 88285 0.864 66471 67633 0.950 21293 16321 0.981 68436 11112 0.993 26205 30009 0.997 52124 78233 0.999 08811 80344 0.999 66453 73720
Y Predicted Y
Y
X Variable 2
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